Autonomous agents that operate in the real world must often deal with partial observability, which is commonly modeled as partially observable Markov decision processes (POMDPs). However, traditional POMDP models rely on the assumption of complete knowledge of the observation source, known as fully observable data association. To address this limitation, we propose a planning algorithm that maintains multiple data association hypotheses, represented as a belief mixture, where each component corresponds to a different data association hypothesis. However, this method can lead to an exponential growth in the number of hypotheses, resulting in significant computational overhead. To overcome this challenge, we introduce a pruning-based approach for planning with ambiguous data associations. Our key contribution is to derive bounds between the value function based on the complete set of hypotheses and the value function based on a pruned-subset of the hypotheses, enabling us to establish a trade-off between computational efficiency and performance. We demonstrate how these bounds can both be used to certify any pruning heuristic in retrospect and propose a novel approach to determine which hypotheses to prune in order to ensure a predefined limit on the loss. We evaluate our approach in simulated environments and demonstrate its efficacy in handling multi-modal belief hypotheses with ambiguous data associations.
翻译:在现实世界运作的自主代理商往往必须处理部分可观察性,通常以部分可观测的Markov决策程序(POMDPs)为模型。然而,传统的POMDP模式依赖于对观测源的完全知识的假设,即完全可观测的数据协会。为解决这一局限性,我们提议一种计划算法,维持多种数据联系假设,作为一种信仰混合体,每个组成部分都代表不同的数据联系假设。然而,这种方法可能导致假设数量的指数增长,从而产生重大的计算间接费用。为了克服这一挑战,我们采用了一种基于细微的办法来规划模糊的数据协会。我们的主要贡献是在完整的假设和基于假设的纯分数的数值功能之间找到界限,使我们能够在计算效率和性能之间建立起一种权衡。我们证明这些界限可以用来证明回溯中的任何超常性,并提议一种新颖的方法,以确定哪些假设是预设的假设,以确保在模拟环境中以预定义的模型化方法展示我们对于模拟损失的认识。</s>